Information processing apparatus, method and non-transitory computer-readable storage medium

ABSTRACT

An information processing apparatus includes a memory, and a processor coupled to the memory and configured to obtain time series data indicating a time-dependent change of a biological signal value after a meal, determine, based on the obtained time series data, a first feature amount of the time-dependent change of the biological signal value after the meal, determine, based on the determined first feature amount, an index value related to the meal, and output the determined index value.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of InternationalApplication PCT/JP2015/081829 filed on Nov. 12, 2015 and designated theU.S., the entire contents of which are incorporated herein by reference.

FIELD

The embodiment discussed herein is related to an information processingapparatus, a method, and a non-transitory computer-readable storagemedium.

BACKGROUND

A technology for estimating an index regarding ingestion has beendesired. For example, a technology by which the amount of a meal isestimated in such a manner that an increase of a heart rate during ameal is focused is disclosed. There is Japanese National Publication ofInternational Patent Application No. 10-504739 as the related art.

SUMMARY

According to an aspect of the invention, an information processingapparatus includes a memory, and a processor coupled to the memory andconfigured to obtain time series data indicating a time-dependent changeof a biological signal value after a meal, determine, based on theobtained time series data, a first feature amount of the time-dependentchange of the biological signal value after the meal, determine, basedon the determined first feature amount, an index value related to themeal, and output the determined index value.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating the hardware configuration of aningestion-index estimation apparatus according to a first embodiment.

FIG. 2A is a block diagram of functions implemented by running a mealdetection program, FIG. 2B is a functional block diagram representingthe functions of a feature-vector calculation unit.

FIG. 3A is a graph illustrating meal-induced time variation of a heartrate, FIG. 3B is a graph illustrating feature points.

FIGS. 4A and 4B are each a graph illustrating area feature amounts.

FIG. 5A is a graph illustrating speed feature amounts, FIG. 5B is agraph illustrating amplitude feature amounts, FIG. 5C is a graphillustrating time feature amounts.

FIGS. 6A and 6B are each a graph illustrating function feature amounts.

FIGS. 7A and 7B are graphs illustrating a method for calculatingestimated calories of ingested food.

FIGS. 8A and 8B are graphs illustrating a method for calculatingestimated remaining calories.

FIG. 9 is a graph illustrating a method for calculating estimateddigestive power.

FIG. 10 is a diagram illustrating a flowchart representing calculationof a function f.

FIG. 11 is a diagram illustrating a flowchart representing estimation ofa caloric intake.

FIG. 12 is a diagram illustrating a flowchart representing a process forcalculating an area II.

FIG. 13 is a graph illustrating a different example of meal-induced timevariation of a heart rate.

FIGS. 14A and 14B are each a diagram illustrating a different apparatusconfiguration of the ingestion-index estimation apparatus.

DESCRIPTION OF EMBODIMENT First Embodiment

FIG. 1 is a block diagram illustrating the hardware configuration of aningestion-index estimation apparatus 100 according to a firstembodiment. As illustrated in FIG. 1, the ingestion-index estimationapparatus 100 includes a central processing unit (CPU) 101, a randomaccess memory (RAM) 102, a memory 103, a display 104, abiological-signal measurement apparatus 105, and other devices. Thesedevices are coupled to each other via a bus.

The CPU 101 is a central processing unit. The CPU 101 includes one ormore cores. The RAM 102 is a volatile memory that temporarily stores aprogram run by the CPU 101, data processed by the CPU 101, and the like.

The memory 103 is a nonvolatile memory. For example, a read only memory(ROM), a solid state drive (SSD) such as a flash memory, a hard diskdriven by a hard disk drive, or the like is usable as the memory 103.The memory 103 stores therein an ingestion-index estimation programaccording to this embodiment. The display 104 is a liquid crystaldisplay, an electroluminescence panel, or the like and displays theresult of an ingestion-index estimation process described later.

The biological-signal measurement apparatus 105 is an apparatus thatmeasures biological signal values of living things that eat, such ashumans and animals. Examples of the biological signalbiological signalvalues include a blood pressure, a body temperature, an electric skinresistance, an electrocardiographic complex, a pulse wave form, a heartrate (pulse rate), and a skin temperature. In this embodiment, forexample, the biological-signal measurement apparatus 105 is an apparatusthat measures the heartbeat (pulse) of a user. For example, thebiological-signal measurement apparatus 105 may be anelectrocardiograph, a pulsation sensor, or another apparatus.

The ingestion-index estimation program stored in the memory 103 isloaded on the RAM 102 to be able to be run. The CPU 101 runs theingestion-index estimation program loaded on the RAM 102. Theingestion-index estimation apparatus 100 thereby executes processes.FIG. 2A is a block diagram of functions implemented by theingestion-index estimation program. By running the ingestion-indexestimation program, a heart-rate acquisition unit 10, a feature-pointextraction unit 20, a feature-vector calculation unit 30, aningestion-index estimation unit 40, and other components areimplemented.

FIG. 2B is a functional block diagram representing the functions of thefeature-vector calculation unit 30. As illustrated in FIG. 2B, thefeature-vector calculation unit 30 functions as an area-feature-amountcalculation unit 31, a speed-feature-amount calculation unit 32, anamplitude-feature-amount calculation unit 33, a time-feature-amountcalculation unit 34, a function-feature-amount calculation unit 35, andother components.

(Ingestion-Index Estimation Process)

The heart-rate acquisition unit 10 acquires heartbeat from thebiological-signal measurement apparatus 105 and thereby acquires timevariation of a heart rate. FIG. 3A illustrates meal-induced timevariation of a heart rate. In FIG. 3A, the horizontal axis representselapsed time, and the vertical axis represents heart rate. The heartrate is a pulsation rate per unit time and is specifically a pulsationrate per minute. Hereinafter, the term “heart rate” denotes a pulsationrate per minute unless otherwise particularly stated.

As illustrated in FIG. 3A, two peaks appear in a meal-inducedheart-rate-rising section. The first peak represents a heart rate changeappearing immediately after the start of a meal. The second peakrepresents a heart rate change appearing over a long span of time fromtime of the meal. For example, the first peak is supposed to beattributed to mastication, swallowing, hand movement, and the like. Thesecond peak is supposed to be attributed to movement of digestive organssuch as a digestive event and absorption. The start time point of therising section at which the rising edge of the first peak is detected isa meal-start time point. For example, a start time point at which therising speed of the heart rate becomes greater than or equal to athreshold and a rising range becomes greater than or equal to athreshold may be detected as the rising edge. The local maximum point ofthe first peak is a meal-end time point. A time point at which the heartrate in the second peak returns to a predetermined value after exceedingthe local maximum point is an end time point of the meal-inducedheart-rate-rising section. For example, a time point at which the heartrate in the second peak returns to the heart rate observed at the startof the meal may be used as the end time point of the heart-rate-risingsection, or a time point at which the heart rate returns to a valueobtained by adding or subtracting a predetermined value to or from theheart rate observed at the meal-start time point may be used as the endtime point of the heart-rate-rising section. The wave form of the heartrate from the start time point of the meal-induced heart-rate-risingsection to the end time point is referred to as a heartbeat peak.

The feature-point extraction unit 20 extracts feature points for featurevector calculation in a heartbeat peak acquired by the heart-rateacquisition unit 10. First, as illustrated in FIG. 3B, the feature-pointextraction unit 20 extracts a feature point i (normally corresponding toa meal start time) at time when the heart rate starts rising due to theinfluence of the meal. For example, the feature-point extraction unit 20detects the aforementioned rising edge and thereby extracts the featurepoint i. Alternatively, a value manually input by the user may be used.Alternatively, since the heart rate is mentally influenced before anactual meal and thus starts rising in some cases, the minimum value ofthe heart rate within a predetermined time (for example, 15 minutes)before the actual meal time may be used as the feature point i.

The feature-point extraction unit 20 then extracts a feature point ii attime when the rising of the heart rate is settled (the rising speed isdecreased) after the meal start. For example, the feature-pointextraction unit 20 performs straight line fitting with the least squaresmethod on data regarding a section from the feature point i to time t1within a predetermined time (for example, within three minutes) afterthe feature point i. If an error between the fitted line and the actualdata is less than or equal to a threshold, the feature-point extractionunit 20 updates the time t1 with t1+δt (for example, δt=10 seconds). Thefeature-point extraction unit 20 repeats this process by using theupdated time t1. The feature-point extraction unit 20 uses, as thefeature point ii, time when the error exceeds the threshold.

The feature-point extraction unit 20 then extracts a feature point iiithat is the local maximum point of the first peak of the heart rateafter the meal start (time when the heart rate starts to lower). Thefeature point iii normally corresponds to a meal end time. However, amethod using the local maximum point includes an error in some cases.Hence, data regarding a section from a point before the local maximumpoint to a point after the local maximum point (for example, dataregarding a section from time five minutes before the local maximumpoint to time five minutes after the local maximum point) may beacquired, the time series data of the heart rate may be divided into twosegments by using the bottom-up algorithm, and then time at the borderof the segments may be extracted as the feature point iii.Alternatively, a manually input value may be used.

The feature-point extraction unit 20 then extracts a feature point iv attime when the lowering of the heart rate is settled (the lowering speedis decreased) in the first peak. For example, the feature-pointextraction unit 20 may use a point a predetermined time after thefeature point i (for example, after 30 minutes) as the feature point iv.Alternatively, the feature-point extraction unit 20 performs thestraight line fitting with the least squares method on data regarding asection from time at the feature point iii to time t1 within apredetermined time (for example, within three minutes) after the featurepoint iii. If an error between the fitted line and the actual data isless than or equal to a threshold, the feature-point extraction unit 20updates the time t1 with t1+δt (for example, δt=10 seconds). Thefeature-point extraction unit 20 repeats this process by using theupdated time t1. The feature-point extraction unit 20 uses, as thefeature point iv, time when the error exceeds the threshold.

The feature-point extraction unit 20 then extracts, as a feature pointv, time when a gentle decrease of a high level heart rate after the endof the meal is started. For example, the feature-point extraction unit20 may extract the local maximum point of the second peak as the featurepoint v. Alternatively, the feature-point extraction unit 20 performsmoving average in a 30-minute window on data regarding a section fromtime a predetermined time (for example, one hour) before the timeacquired as the feature point i and time a predetermined time (forexample, four hours) after the time acquired as the feature point i.Based on this, the feature-point extraction unit 20 may use, as thefeature point v, time corresponding to the maximum value of the movingaverage data regarding a section within a predetermined time (forexample, within one hour) after the time of the feature point iii.

The feature-point extraction unit 20 then extracts, as a feature pointvi, time (the end time of the heartbeat peak) when the gentle decreaseof the high level heart rate after the meal end ceases. For example, thefeature-point extraction unit 20 performs the straight line fitting withthe least squares method on data regarding a section from time at thefeature point v to time t1 within a predetermined time (for example,within one hour) after the feature point v. If an error between thefitted line and the actual data is less than or equal to a threshold,the feature-point extraction unit 20 updates the time t1 with t1+δt (forexample, δt=10 seconds). The feature-point extraction unit 20 repeatsthis process by using the updated time t1. The feature-point extractionunit 20 uses, as the feature point vi, time when the error exceeds thethreshold.

Next, the feature-vector calculation unit 30 calculates a feature vectorrelated to ingestion by using the feature points extracted by thefeature-point extraction unit 20. The feature vector includes one ormore feature amounts. First, the area-feature-amount calculation unit 31calculates area feature amounts. The area feature amounts include atleast an integration value of the time variation of a biologicalsignalbiological signal value after the meal end. For example, asillustrated in FIG. 4A, the area-feature-amount calculation unit 31calculates an area I of the first peak and an area II of the second peakby using integration. The area I is an area from the feature point i tothe feature point iv. The area II is an area from the feature point ivto the feature point vi. Next, as illustrated in FIG. 4B, thearea-feature-amount calculation unit 31 calculates an area III from themeal start to the meal end and a peak area IV from the meal end to apoint where the heart rate change line becomes flat. The area III is anarea from the feature point i to the feature point iii. The area IV isan area from the feature point iii to the feature point vi. Thearea-feature-amount calculation unit 31 then calculates an area Vbetween any time and time in one of the areas I to IV. Note that theareas Ito V are each the area of ΔHR that is an increase from apredetermined heart rate. The heart rate at the feature point i, theheart rate at the feature point vi, or the like may be used as thepredetermined heart rate.

Next, as illustrated in FIG. 5A, the speed-feature-amount calculationunit 32 calculates speed feature amounts. The speed feature amountsinclude at least one of a rising speed and a lowering speed of at leasta section between a point in time variation of the biologicalsignalbiological signal value and a point after the meal end in the timevariation of the biological signalbiological signal value. For example,the speed-feature-amount calculation unit 32 calculates a speed I atwhich the level of the heart rate becomes high and is kept unchanged tosome extent after the meal start. The speed I is, for example, a risingspeed of the heart rate from the feature point i to the feature pointii. The speed-feature-amount calculation unit 32 then calculates a speedII at which the level of the heart rate approaches, after the meal end,to the level of the heart rate observed before the meal start. The speedII is, for example, a lowering speed of the heart rate from the featurepoint iii to the feature point iv. The speed-feature-amount calculationunit 32 then calculates a speed III of rising from a point before themeal start to the second peak. The speed III is, for example, a risingspeed calculated from a line connecting the feature point i and thefeature point v. The speed-feature-amount calculation unit 32 thencalculates a speed IV at which the raised level of the heartbeatobserved after the meal end for a long time approaches to the originallevel observed before the meal start. The speed IV is, for example, alowering speed of the heart rate in a section from the feature point vto the feature point vi. The speed-feature-amount calculation unit 32then calculates a rising speed or a lowering speed of the heart rate atany time as a speed V.

Next, as illustrated in FIG. 5B, the amplitude-feature-amountcalculation unit 33 calculates amplitude feature amounts. The amplitudefeature amounts include at least one of a rising range and a loweringrange of at least a section between a point in time variation of thebiological signalbiological signal value and a point after the meal endin the time variation of the biological signalbiological signal value.For example, the amplitude-feature-amount calculation unit 33 calculatesan amplitude I from the level of a heart rate observed at a point beforethe meal start to a high level observed at a point during the meal. Theamplitude I corresponds to, for example, a rising range from the featurepoint i to the feature point ii. The amplitude-feature-amountcalculation unit 33 then calculates an amplitude II between a heart rateat the meal end and a heart rate observed when the level of the heartrate approaches, after the meal end, to the level of the heart ratebefore the meal start. The amplitude II corresponds to, for example, alowering range from the feature point iii to the feature point iv. Theamplitude-feature-amount calculation unit 33 then calculates anamplitude III from a point before the meal start to the second peak. Theamplitude III corresponds to, for example, a rising range from thefeature point i to the feature point v. The amplitude-feature-amountcalculation unit 33 then calculates an amplitude IV in which the levelof a heart rate that is high and observed for a long time in the secondpeak approaches to the original level before the meal start and thenbecomes steady. The amplitude IV corresponds to, for example, a loweringrange from the feature point v to the feature point vi. Theamplitude-feature-amount calculation unit 33 then calculates anamplitude V from a point before the meal start to any time.

Next, as illustrated in FIG. 5C, the time-feature-amount calculationunit 34 calculates time feature amounts. The time feature amountsinclude at least a time length between a point in time variation of thebiological signalbiological signal value and a point after the meal endin the time variation of the biological signalbiological signal value.For example, the time-feature-amount calculation unit 34 calculates atime period I from the meal start to a point when the heart rate isstabilized to the high level heart rate observed during the meal. Thetime period I corresponds to, for example, a time length from thefeature point i to the feature point ii. The time-feature-amountcalculation unit 34 then calculates a time period II from the meal startto the meal end. The time period II corresponds to, for example, a timelength from the feature point i to the feature point iii. Thetime-feature-amount calculation unit 34 then calculates a time periodIII in which the level of the heart rate after the meal start becomesthe high level observed during the meal. The time period III correspondsto, for example, a time period from the feature point ii to the featurepoint iii. The time-feature-amount calculation unit 34 then calculates atime period IV in which the level of the heart rate approaches, afterthe meal end, to the level before the meal start. The time period IVcorresponds to, for example, a time period from the feature point iii tothe feature point iv. The time-feature-amount calculation unit 34 thencalculates a time period V from the meal start to the second peak. Thetime period V corresponds to, for example, a time length from thefeature point i to the feature point v. The time-feature-amountcalculation unit 34 then calculates a time period VI from the localmaximum point of the second peak to the end of the second peak. The timeperiod VI corresponds to, for example, a time length from the featurepoint v to the feature point vi. The time-feature-amount calculationunit 34 then calculates a time period VII between time used as one ofthe times Ito VI and any time.

Next, the function-feature-amount calculation unit 35 calculatesfunction feature amounts. Note that the meal-induced heart-ratevariation over a long span of time is supposed to be caused by aplurality of factors. For example, digestion and absorption time varieswith the nutrient, and it is thus conceivable that the heart ratevariation varies as the result of this. Hence, in this embodiment, it isassumed that the heart-rate variation over a long span of time that isbased on a heart rate and induced by a meal is dividable into functionson a per-factor basis, and the area feature amounts, the speed featureamounts, the amplitude feature amounts, the time feature amounts, orvalues similar to these are calculated as feature amounts. If occurrenceof long-term heart rate variation caused by, for example, three factorsis assumed, the conceivable way of dividing is as follows.

First, as illustrated in FIG. 6A, the second peak may be divided intothree time ranges A to C. For example, the area of ΔHR(t) of a timerange applying “the start time of the second peak (feature pointiv)≤t<a” may be used as the area of the time range A. The area of ΔHR(t)of a time range applying “a≤t<b” may be used as the area of the timerange B. The area of ΔHR(t) of a time range applying “b≤t< the end timeof the second peak (feature point vi)” may be used as the area of thetime range C. The area of each of the time ranges A to C may be used asan area feature amount.

Alternatively, as illustrated in FIG. 6B, the above-described threefactors may be expressed as Gaussian functions, respectively, andparameters p_(i) to p₃ expressed in accordance with the followingformula may be determined to obtain the smallest error from data havingthe total of the three Gaussian functions. Note that the parameters p₁to p₃ are ΔHR at times t1 to t3 at the peak points of the three Gaussianfunctions, respectively. The times t1 to t3 and σ₁ to σ₃ may have beendetermined based on previous knowledge. Note that the parameters p₁ top₃ may be used as the amplitude feature amounts. If a motion section isacquirable based on an inertial sensor or the like, the time variationof the heart rate used for feature points i to vi or the feature vectorcalculation may be obtained by removing an influence of the motion fromthe heart rate in the motion section. Features of time variation of aheart rate that is induced by a meal to a greater extent may thereby beacquired. For example, it is conceivable that such processing asdeleting heartbeat data in the motion section and then performing linearinterpolation after the deletion is performed.

${\Delta \; {{HR}(t)}} = {\sum\limits_{i = 1}^{3}{p_{i} \star {\exp \left( {- \frac{\left( {t - t_{i}^{\prime}} \right)^{2}}{2\sigma_{i}^{2}}} \right)}}}$

The ingestion-index estimation unit 40 calculates an index (ingestionindex) regarding the condition of a person, an eating behavior, ingestedfood, and the like and regarding a relationship thereamong by using thecalculated feature vector. The ingestion index is, for example, caloriesrelated to food or metabolism. Further, examples of ingestion indexinclude a caloric intake. The ingestion index may be expressed as afunction of the feature vector. Accordingly, in a case where the featurevector is x, the ingestion index may be expressed as f(x). Note that afunction f may be based on knowledge given in advance or may be a modelbuilt up based on a relationship with known data. The function f mayalso be prepared for a person, place, hour, or the like. For example,the function f may also be prepared separately for each of breakfast,lunch, dinner, and snacking. In addition, the ingestion index includesnot only calories related to food or metabolism but also a bodily changerelated to a meal, a change of working of the brain or a bodily organ, achange of blood flow or a blood component, the content of a meal, thedegree of content of an ingestion action, and the like. Specifically, asthe ingestion index, digestibility, the degree of hunger, the amount ofa meal, the degree of quick eating, the degree of slow eating, anutrient intake, a blood sugar level, a body temperature rise, acalorific value, digestive organ movement, the degree of mastication,perspiration, a digestion load index, and the like are cited. Note thatcalories of ingested food include not only calories absorbed by the bodybut also calories caused by physical combustion of the ingested food andthe like and may be estimated in such a manner that Atwater coefficientsor the like is considered. When the ingestion index is calculated, thefeature vector may be calculated by combining feature amounts derivedfrom a plurality of pieces of biological information. Further,information other than a biological signal may be added to the featurevector.

Subsequently, a specific example of a method for calculating estimatedcalories of ingested food will be described. For example, as illustratedin FIG. 7A, the area II of the second peak calculated by thearea-feature-amount calculation unit 31 may be used as the featurevector x. For example, a relationship between the feature vectorregarding a known meal and a caloric intake is learned from existingdata by using a fitted curve. The learning fitted curve may be generatedas the function f. FIG. 7B is a graph illustrating the function f.Estimated calories for unknown calories of a meal may be calculated fromthe feature vector x by using the generated function f.

A state varying with the progress of digestion may be reflected in thecalories of ingested food. For example, (x₁, x₂, x₃)=(the area of thetime range A, the area of the time range B, and the area of the timerange C) may be used as the feature vector x. The areas are illustratedin FIG. 6A, and f(x)=a₁×x₁+a₂×x₂+a₃×x₃+a₄ may be expressed. Parametersa_(i) to a₄ may be acquired from relationships between the featurevector acquired from existing data and calories.

Estimated remaining calories may be calculated as the ingestion index.The estimated remaining calories are intake calories having not absorbedyet at a focused time of a total intake calories. For example, (x₁,x₂)=(the area II of the second peak, the area from the start time of thesecond peak to the focused time) may be used as the feature vector x.FIG. 8A is a graph illustrating the area II of the second peak. FIG. 8Bis a graph illustrating the area V from the start time of the secondpeak to the focused time. In this case, f(x)=1−x₂/x₁ may be expressed.The use of the function f enables estimated remaining calories at thefocused time to be calculated.

Estimated digestive power may be calculated as the ingestion index. Notethat the digestive power represents an ability to digest. For example,if time taken to digest and absorb food is short despite a high indexrelated to the total amount of eaten food, it can be said that thedigestive power is high. Hence, for example, (x₁, x₂, x₃, x₄)=(the areaI, the area II, the time period V, the time period VI) may be used asthe feature vector x. FIG. 9 is a graph illustrating these featureamounts. In a case where this feature vector x is used, (x₁+x₂)/(x₃+x₄)may be used as the function f(x).

Subsequently, a specific example of calculating an estimated caloricintake will be described with reference to a flowchart. FIG. 10 is anexample of a flowchart representing a process for calculating thefunction f. As illustrated in FIG. 10, the heart-rate acquisition unit10 acquires heart rates from the biological-signal measurement apparatus105 (step S1). For example, the heart-rate acquisition unit 10 acquiresa heart rate every minute.

Subsequently, the feature-point extraction unit 20 acquires meal timesof respective meals (step S2). Each meal time includes at least one of ameal start time and a meal end time. Subsequently, the ingestion-indexestimation unit 40 acquires the calories of each meal (step S3). Forexample, the ingestion-index estimation unit 40 acquires calories inputby the user. Subsequently, the feature-vector calculation unit 30calculates a feature vector from the heart rates for each meal (stepS4). The area II is herein calculated. Subsequently, the ingestion-indexestimation unit 40 performs straight line fitting on a relationshipbetween the calories and the area II by using the least squares methodfor each meal and acquires the gradient and intercept of the fitted line(step S5). Performing the steps in the flowchart enables a learningmodel of the relationship between the caloric intake and the area II tobe acquired in advance.

Subsequently, a specific example in which a caloric intake is estimatedby using the acquired learning model will be described with reference toa flowchart. FIG. 11 is an example of a flowchart representing a processfor estimating a caloric intake. As illustrated in FIG. 11, theheart-rate acquisition unit 10 acquires heart rates from thebiological-signal measurement apparatus 105 (step S11). Subsequently,the feature-point extraction unit 20 acquires meal times of therespective meals (step S12). For example, the feature-point extractionunit 20 acquires each meal time of the corresponding meal afterextracting the feature points i to vi. Subsequently, the feature-vectorcalculation unit 30 calculates a feature vector for each meal from theheart rates (step S13). The area II is herein calculated. Subsequently,the ingestion-index estimation unit 40 calculates estimated calories byapplying the area II to the learning model acquired in advance (stepS14).

FIG. 12 is an example of a flowchart representing a process forcalculating the area II. As illustrated in FIG. 12, the feature-vectorcalculation unit 30 acquires, from the heart-rate acquisition unit 10,heart rates in a section from time a predetermined time before (forexample, 15 minutes before) the meal start time to the meal start time(step S21). The feature-vector calculation unit 30 then sets time havingthe minimum value of the heart rates acquired in step S21 as t0 (stepS22). Step S22 is processing for extracting the feature point i.

The feature-vector calculation unit 30 then acquires, from theheart-rate acquisition unit 10, heart rates in the section from thefeature point iv after the meal start time (for example, one hour afterthe meal start time) to the feature point vi (for example, four hoursafter the meal start time) (step S23). The feature-vector calculationunit 30 may thereby acquire heart rate data regarding the section fromthe feature point iv to the feature point vi. The feature-vectorcalculation unit 30 then calculates a difference between each acquiredheart rate at the corresponding time and the heart rate at the time t0(step S24). The feature-vector calculation unit 30 then calculates thesum of the calculated heart rate differences (step S25). Thefeature-vector calculation unit 30 acquires the value calculated in stepS25 as the area II (step S26).

According to this embodiment, an index related to ingestion is estimatedin meal-induced time variation of a biological signalbiological signalvalue by using the feature amounts of the time variation of thebiological signal value after the end of a meal. In this case,meal-induced variation of the biological signal value over a long spanof time is used. That is, biological signal value variation induced by adigestive event, absorption, or the like is used. The accuracy of theingestion index estimation may thereby be enhanced.

Calculating an integration value (the area) in the time variation of thebiological signal value after the meal end enables the calculated valueto be used as a feature amount. Calculating at least one of a risingspeed and a lowering speed in a section between a point in timevariation of the biological signal value and a point after the meal endin the time variation of the biological signal value enables thecalculated value to be used as a feature amount. Calculating at leastone of a rising range and a lowering range in a section between a pointin time variation of the biological signal value and a point after themeal end in the time variation of the biological signal value enablesthe calculated value to be used as a feature amount. Calculating a timelength of a section between a point in time variation of the biologicalsignal value and a point after the meal end in the time variation of thebiological signal value end enables the calculated value to be used as afeature amount. Calculating a plurality of function values based on acase where at least one of the above-described feature amounts is thesum of the plurality of function values enables the calculated value tobe used as a new feature amount.

(Caloric Intake Evaluation Example)

For a comparison purpose, a caloric intake is estimated by using a peakarea from the meal start to the meal end. Although a correlation betweena peak area and a caloric intake is obtained to a certain extent, thecorrelation coefficient has a small value. That is, only a lowcorrelation is obtained. In contrast, in a case where a caloric intakeis estimated by using the area II after the meal end, the correlationcoefficient of the correlation between the caloric intake and the areaII has a value 1.5 to 2 times larger than the value in the comparativecase. That is, a higher correlation is obtained. It is conceivable thatthe use of meal-induced variation of the biological signal value over along span of time leads to enhancement of the accuracy of the ingestionindex.

(Different Example of Meal-induced Time Variation of Heart-rate)

FIG. 13 is a graph illustrating a different example of meal-induced timevariation of a heart rate. As illustrated in FIG. 13, there is a casewhere the local maximum point of the second peak does not appear in theheart-rate-rising section (heartbeat peak). In this case, the featurepoint iv at which the decrease of the heart rate is settled in the firstpeak and the feature point v at which the gentle decrease of the highlevel heart rate after the meal end is started are approximatelyidentical. Hence, in the case where the second peak does not appear asin FIG. 13, the feature point iv and the feature point v may be used onthe assumption that the feature point iv and the feature point v are thesame time.

FIGS. 14A and 14B are each a diagram illustrating a different apparatusconfiguration of a corresponding one of the ingestion-index estimationapparatus 100 and an ingestion-index estimation apparatus 100 a. Asillustrated in FIG. 14A, an ingestion-index estimation apparatus may beconfigured such that a server and a wearable device wirelessly exchangedata, the server including the CPU 101, the RAM 102, the memory 103, anda wireless apparatus 106, the wearable device including the display 104,the biological-signal measurement apparatus 105, and a wirelessapparatus 107. In addition, as illustrated in FIG. 14B, theingestion-index estimation apparatus may be configured such that aserver, a terminal, and a wearable device wirelessly exchange data, theserver including the CPU 101, the RAM 102, the memory 103, and thewireless apparatus 106, the terminal including the display 104 and thewireless apparatus 107, the wearable device including thebiological-signal measurement apparatus 105 and a wireless apparatus108.

Note that in the embodiment described above, the feature-pointextraction unit 20 and the feature-vector calculation unit 30 eachfunction as an example of a feature-amount extraction unit that extractsa feature amount of time variation of a biological signal value afterthe end of a meal in meal-induced time variation of the biologicalsignal value. The ingestion-index estimation unit 40 functions as anexample of an index estimation unit that estimates an index related toingestion by using the feature amount extracted by the feature-amountextraction unit.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the inventionand the concepts contributed by the inventor to furthering the art, andare to be construed as being without limitation to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although the embodiment of the presentinvention has been described in detail, it should be understood that thevarious changes, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the invention.

What is claimed is:
 1. An information processing apparatus comprising: amemory; and a processor coupled to the memory and configured to: obtaintime series data indicating a time-dependent change of a biologicalsignal value after a meal; determine, based on the obtained time seriesdata, a first feature amount of the time-dependent change of thebiological signal value after the meal; determine, based on thedetermined first feature amount, an index value related to the meal; andoutput the determined index value.
 2. The information processingapparatus according to claim 1, wherein the first feature amount is anintegration value of the biological signal value after the meal.
 3. Theinformation processing apparatus according to claim 1, wherein the firstfeature amount is speed of the time-dependent change of the biologicalsignal value.
 4. The information processing apparatus according to claim1, wherein the first feature amount is a range of the time-dependentchange of the biological signal value.
 5. The information processingapparatus according to claim 1, wherein the processor is configured todetermine a plurality of function values, the time-dependent change ofthe biological signal value being indicated as a sum of the plurality offunction values.
 6. The information processing apparatus according toclaim 1, wherein the biological signal value indicates a heart rate. 7.The information processing apparatus according to claim 1, wherein theindex value indicates at least one of a calorie included in the meal anda calorie related to metabolism.
 8. A method executed by a computer, themethod comprising: obtaining time series data indicating atime-dependent change of a biological signal value after a meal;determining, based on the obtained time series data, a first featureamount of the time-dependent change of the biological signal value afterthe meal; determining, based on the determined first feature amount, anindex value related to the meal; and outputting the determined indexvalue.
 9. The method according to claim 8, wherein the first featureamount is an integration value of the biological signal value after themeal.
 10. The method according to claim 8, wherein the first featureamount is speed of the time-dependent change of the biological signalvalue.
 11. The method according to claim 8, wherein the first featureamount is a range of the time-dependent change of the biological signalvalue.
 12. The method according to claim 8, further comprising:determining a plurality of function values, the time-dependent change ofthe biological signal value being indicated as a sum of the plurality offunction values.
 13. The method according to claim 8, wherein thebiological signal value indicates a heart rate.
 14. The method accordingto claim 8, wherein the index value indicates at least one of a calorieincluded in the meal and a calorie related to metabolism.
 15. Anon-transitory computer-readable storage medium storing a program thatcauses an information processing apparatus to execute a process, theprocess comprising: obtaining time series data indicating atime-dependent change of a biological signal value after a meal;determining, based on the obtained time series data, a first featureamount of the time-dependent change of the biological signal value afterthe meal; determining, based on the determined first feature amount, anindex value related to the meal; and outputting the determined indexvalue.
 16. The non-transitory computer-readable storage medium accordingto claim 15, wherein the first feature amount is an integration value ofthe biological signal value after the meal.
 17. The non-transitorycomputer-readable storage medium according to claim 15, wherein thefirst feature amount is speed of the time-dependent change of thebiological signal value.
 18. The non-transitory computer-readablestorage medium according to claim 15, wherein the first feature amountis a range of the time-dependent change of the biological signal value.19. The non-transitory computer-readable storage medium according toclaim 15, wherein the biological signal value indicates a heart rate.20. The non-transitory computer-readable storage medium according toclaim 15, wherein the index value indicates at least one of a calorieincluded in the meal and a calorie related to metabolism.